Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	New York	19,681
2	New Jersey	18,482
3	Massachusetts	15,007
4	Rhode Island	14,575
5	District of Columbia	13,222
6	Connecticut	12,332
7	Delaware	10,209
8	Illinois	10,106
9	Maryland	9,691
10	Louisiana	9,234
11	Nebraska	8,082
12	Iowa	6,907
13	Michigan	6,470
14	Pennsylvania	6,249
15	South Dakota	6,147
16	Virginia	5,937
17	Mississippi	5,802
18	Indiana	5,693
19	Minnesota	4,946
20	Colorado	4,859
21	Georgia	4,649
22	New Mexico	4,263
23	Alabama	4,180
24	Tennessee	3,925
25	Utah	3,769
26	North Dakota	3,758
27	New Hampshire	3,708
28	Arizona	3,699
29	Kansas	3,620
30	Wisconsin	3,598
31	North Carolina	3,400
32	California	3,340
33	Ohio	3,291
34	Washington	3,291
35	Nevada	3,140
36	Arkansas	3,123
37	Florida	2,976
38	South Carolina	2,774
39	Texas	2,619
40	Kentucky	2,550
41	Missouri	2,441
42	Maine	1,911
43	Oklahoma	1,807
44	Idaho	1,764
45	Vermont	1,703
46	Wyoming	1,636
47	Puerto Rico	1,560
48	West Virginia	1,196
49	Oregon	1,140
50	Alaska	803
51	Montana	509
52	Hawaii	468

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Arizona	188
2	Michigan	180
3	Utah	127
4	Maryland	115
5	Arkansas	110
6	Virginia	110
7	North Carolina	107
8	District of Columbia	100
9	Alabama	97
10	Minnesota	95
11	New Mexico	93
12	Iowa	89
13	Louisiana	89
14	Nebraska	89
15	South Carolina	82
16	Mississippi	79
17	California	76
18	Illinois	76
19	South Dakota	71
20	Tennessee	70
21	Connecticut	68
22	Delaware	67
23	North Dakota	67
24	Massachusetts	66
25	Indiana	65
26	New Jersey	61
27	Nevada	60
28	Florida	58
29	Washington	58
30	Georgia	57
31	Texas	53
32	Wisconsin	50
33	New York	49
34	Puerto Rico	49
35	Kentucky	45
36	New Hampshire	40
37	Pennsylvania	40
38	Colorado	37
39	Missouri	37
40	Rhode Island	36
41	Ohio	34
42	Maine	30
43	Oregon	26
44	Kansas	24
45	Alaska	20
46	Oklahoma	20
47	Vermont	19
48	Idaho	18
49	Wyoming	14
50	West Virginia	7
51	Hawaii	4
52	Montana	2

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New York	1,551
2	New Jersey	1,370
3	Connecticut	1,141
4	Massachusetts	1,061
5	Rhode Island	728
6	District of Columbia	692
7	Louisiana	631
8	Michigan	590
9	Illinois	468
10	Pennsylvania	468
11	Maryland	454
12	Delaware	408
13	Indiana	342
14	Mississippi	274
15	Colorado	265
16	Minnesota	212
17	New Hampshire	210
18	Ohio	203
19	Georgia	202
20	Iowa	192
21	New Mexico	188
22	Virginia	172
23	Washington	152
24	Arizona	143
25	Nevada	142
26	Alabama	141
27	Missouri	134
28	Florida	125
29	California	117
30	Wisconsin	111
31	Kentucky	107
32	South Carolina	106
33	Nebraska	101
34	North Dakota	98
35	North Carolina	97
36	Vermont	88
37	Oklahoma	87
38	Kansas	80
39	Maine	73
40	South Dakota	73
41	Texas	63
42	Tennessee	60
43	Arkansas	51
44	Idaho	46
45	West Virginia	46
46	Puerto Rico	44
47	Oregon	38
48	Utah	37
49	Wyoming	29
50	Montana	16
51	Hawaii	12
52	Alaska	10

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Michigan	9
2	New Jersey	7
3	District of Columbia	6
4	Connecticut	5
5	Massachusetts	5
6	Rhode Island	5
7	Delaware	4
8	Illinois	4
9	Maryland	4
10	Minnesota	4
11	Indiana	3
12	Louisiana	3
13	New Hampshire	3
14	Pennsylvania	3
15	Alabama	2
16	Arizona	2
17	Mississippi	2
18	New Mexico	2
19	New York	2
20	North Dakota	2
21	California	1
22	Florida	1
23	Georgia	1
24	Iowa	1
25	Ohio	1
26	South Carolina	1
27	Virginia	1
28	Wisconsin	1
29	Alaska	0
30	Arkansas	0
31	Colorado	0
32	Hawaii	0
33	Idaho	0
34	Kansas	0
35	Kentucky	0
36	Maine	0
37	Missouri	0
38	Montana	0
39	Nebraska	0
40	Nevada	0
41	North Carolina	0
42	Oklahoma	0
43	Oregon	0
44	Puerto Rico	0
45	South Dakota	0
46	Tennessee	0
47	Texas	0
48	Utah	0
49	Vermont	0
50	Washington	0
51	West Virginia	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	123,538	1	99
Lake	Tennessee	103,193	2	99
Dakota	Nebraska	85,389	3	99
Lincoln	Arkansas	75,630	4	99
Nobles	Minnesota	73,605	5	99
Davidson	Tennessee	8,868	251	92
Richland	South Carolina	4,204	667	78
Pierce	Washington	2,577	1052	66
Orange	California	2,386	1122	64
York	South Carolina	1,655	1461	53

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	3,074	1	99
Terrell	Georgia	3,048	2	99
Early	Georgia	3,042	3	99
Randolph	Georgia	2,803	4	99
Northampton	Virginia	2,220	5	99
Richland	South Carolina	164	626	80
Davidson	Tennessee	105	828	73
Pierce	Washington	94	885	71
Orange	California	56	1190	62
York	South Carolina	36	1409	55

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.